Corpus Content

Singing in the Shower Playlists:

Playlist Name ID Songs
Songs to Sing in the Shower 37i9dQZF1DWSqmBTGDYngZ 70
Shower / sing-a-long 1rmsEzwr6ZmRNzCUph24vZ 92
Shower 1dTgSkYRwILdvmckibB9AP 440

Pop Playlists:

Playlist Name ID Songs
Pop Hits 2000-2018 6mtYuOxzl58vSGnEDtZ9uB 291
Pop Hits Rewind 0RPcfl1sCsJ03B0bztuKAn 70

This corpus will represent playlists with generally similar songs, but that are or are not classified as ‘singing in the shower’ playlists. By comparing these groups, I will try to see if there is a measurable difference in certain attributes measured by Spotify, to define what it means to be a ‘singing in the shower’ playlist. I chose the most-followed ‘singing in the shower’ playlists on Spotify, so I believe it should be a good representation of music that people do enjoy while showering.

Examining the Energy and Valence for Both Sets of Playlists


     Looking at this first scatterplot, we see the greatest concentration of music, for both playlists, in the quadrant with high energy and high valence. This seems valid, given both playlists seem to have a large mix of fast-paced, loud, and cheerful music. As such, we can see our first real similarity in both playlists.

     More interestingly, we see that the ‘singing in the shower’ playlists have a wider array of music. Particularly, it seems to have a decent proportion of songs with 0-0.5 valence, as compared to the ‘pop’ playlist. One theory for this is that top charts ‘pop’ music is generally “catchy” because it has a positive and fast beat, but songs that people generally like to sing along to can be happy or more angry, that usually have fast and loud beats. Neither, however, seem to have much music with low energy and low valence, but music in that category tends to be perceived as sadder, which makes it less likely to be considered a ‘pop’ song, and also most likely makes it not as fun to sing in the shower (though this is mere speculation on my behalf).

Comparing the Danceability and Loudness for Both Sets of Playlists


Densities of Track-Level Features